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Abstract The zonal gradients in sea surface temperature and convective heating across the tropical Pacific play a pivotal role in setting the weather and climate patterns globally. Under global warming, the current generation of climate models predict that the zonal gradients will decrease, but the trajectory of the observed trends is the opposite. Theories supporting either of the two projections exist, but there are many relevant processes whose net effect is unclear. In this study, a global constraint – the maximum material entropy production (maxMEP) hypothesis—is considered to help close the gap. The climate system considered here is comprised of a one-layer atmosphere and surface in six regions that represent the western tropical Pacific, eastern tropical Pacific, northern and southern midlatitudes, and northern and southern polar regions. The model conserves energy but does not explicitly include dynamics. The model input is observation-based radiative parameters. The radiative effect of greenhouse gas (GHG) loading is mimicked by prescribing increases in the longwave absorptivity$$\epsilon$$ $ϵ$. The model solutions predict that zonal contrasts in surface temperature, convective heat flux, and surface pressure increase with increasing$$\epsilon$$ $ϵ$. While maxMEP solutions in general cannot provide a definite answer to the problem, these model results strengthen the possibility that the trajectory of the observed trend reflects the response to increasing GHG loading in the atmosphere. 

Dual rail adiabatic circuit design offers hardware-level protection against side-channel power analysis attacks such as Differential Power Analysis (DPA) and Correlation Power Analysis (CPA) attacks. While considerable attention has been given to synthesizing logic tree-based adiabatic circuits, comparatively little attention has been given to generating truly secure circuit variants. This paper presents preliminary results for a secure dual rail adiabatic synthesis tool based on Binary Decision Diagrams (BDDs). The tool demonstrates encouraging performance in matching known optimal transistor counts for several basic logic gates, in addition to providing improvement over existing works on established benchmarks. 

Abstract As the community increases climate model horizontal resolutions and experiments with removing moist convective parameterizations entirely, it is imperative to understand how these advances affect the InterTropical Convergence Zone (ITCZ). We investigate how the ITCZ responds to deactivating parameterized convection at two resolutions, 50 and 6 km, in fixed sea surface temperature, aquaplanet simulations with the NOAA GFDL AM4 atmospheric model. Disabling parameterized convection at 50 km resolution narrows the ITCZ and increases its precipitation minus evaporation (P–E) maximum by ∼78%, whereas at 6 km resolution doing so widens the ITCZ and decreases its P–E maximum by ∼50%. Using the column‐integrated moist static energy budget, we decompose these tropical P–E responses into contributions from changes in atmospheric energy input (AEI), gross moist stability, and gross moisture stratification. At 6 km, the ITCZ weakens due to increased gross moist stability. Disabling the convective parameterization at this finer resolution deepens the circulation, favoring more efficient poleward energy transport out of the deep tropics and reduced precipitation in the core of the ITCZ. Conversely, at 50 km the ITCZ strengthening is primarily driven by AEI, which in turn stems primarily from increased low cloud amount and thus longwave cloud radiative cooling in the Hadley cell subsiding branch. The Hadley circulation overturning intensifies to produce poleward energy fluxes that compensate the longwave cooling, yielding a stronger ITCZ. We further show that the low level diabatic heating profiles over the descending region are instrumental in understanding such diverse responses. 

Abstract Recent years have witnessed extreme heatwaves in Europe and western North America. This study shows that these regions stand out in the zonally asymmetric component of the long-term trend of boreal summer surface temperature, and that intraseasonal timescale processes play an important role in shaping the zonally asymmetric trend pattern. However, these two regions have warmed by different mechanisms. Over Europe, the warming is mostly caused by the positive trend of the net (downward minus upward) surface shortwave radiation weighted by its intraseasonal timescale connection with the skin temperature. The long-term warming in western North America has been caused by the declining surface latent heat flux (weakened evaporative cooling) weighted by its intraseasonal connection with the skin temperature. These mechanisms are consistent with those identified in earlier studies of individual extreme events in the two regions, indicating that part of the long trends are a manifestation of extreme events. The overall findings indicate that to make accurate projections of regional climate change using climate model simulations, it is critical to ensure that the models also accurately simulate intraseasonal variability. 

Proton beam therapy is a unique form of radiotherapy that utilizes protons to treat cancer by irradiating cancerous tumors, while avoiding unnecessary radiation exposure to surrounding healthy tissues. Real-time imaging of the proton beam can make this form of therapy more precise and safer for the patient during delivery. The use of Compton cameras is one proposed method for the real-time imaging of prompt gamma rays that are emitted by the proton beams as they travel through a patient’s body. Unfortunately, some of the Compton camera data is flawed and the reconstruction algorithm yields noisy and insufficiently detailed images to evaluate the proton delivery for the patient. Previous work used a deep residual fully connected neural network. The use of recurrent neural networks (RNNs) has been proposed, since they use recurrence relationships to make potentially better predictions. In this work, RNN architectures using two different recurrent layers are tested, the LSTM and the GRU. Although the deep residual fully connected neural network achieves over 75% testing accuracy and our models achieve only over 73% testing accuracy, the simplicity of our RNN models containing only 6 hidden layers as opposed to 512 is a significant advantage. Importantly in a clinical setting, the time to load the model from disk is significantly faster, potentially enabling the use of Compton camera image reconstruction in real-time during patient treatment. 

Abstract A thermodynamic energy budget analysis is applied to the lowest model level of the ERA5 dataset to investigate the mechanisms that drive the growth and decay of extreme positive surface air temperature (SAT) events. Regional and seasonal variation of the mechanisms are investigated. For each grid point on Earth’s surface, a separate composite analysis is performed for extreme SAT events, which are days when temperature anomaly exceeds the 95th percentile. Among the dynamical terms, horizontal temperature advection of the climatological temperature by the anomalous wind dominates SAT anomaly growth over the extratropics, while nonlinear horizontal temperature advection is a major factor over high-latitude regions and the adiabatic warming is important over major mountainous regions. During the decay period, advection of the climatological temperature by the anomalous wind sustains the warming while nonlinear advection becomes the dominant decay mechanism. Among diabatic heating processes, vertical mixing contributes to the SAT anomaly growth over most locations while longwave radiative cooling hinders SAT anomaly growth, especially over the ocean. However, over arid regions during summer, longwave heating largely contributes to SAT anomaly growth while the vertical mixing dampens the SAT anomaly growth. During the decay period, both longwave cooling and vertical mixing contribute to SAT anomaly decay with more pronounced effects over the ocean and land, respectively. These regional and seasonal characteristics of the processes that drive extreme SAT events can serve as a benchmark for understanding the future behavior of extreme weather. 

Abstract The wintertime (December–February) 1990–2016 Arctic surface air temperature (SAT) trend is examined using self-organizing maps (SOMs). The high-dimensional SAT dataset is reduced into nine representative SOM patterns, with each pattern exhibiting a decorrelation time scale of about 10 days and having about 85% of its variance coming from intraseasonal time scales. The trend in the frequency of occurrence of each SOM pattern is used to estimate the interdecadal Arctic winter warming trend associated with the SOM patterns. It is found that trends in the SOM patterns explain about one-half of the SAT trend in the Barents and Kara Seas, one-third of the SAT trend around Baffin Bay, and two-thirds of the SAT trend in the Chukchi Sea. A composite calculation of each term in the thermodynamic energy equation for each SOM pattern shows that the SAT anomalies grow primarily through the advection of the climatological temperature by the anomalous wind. This implies that a substantial fraction of Arctic amplification is due to horizontal temperature advection that is driven by changes in the atmospheric circulation. An analysis of the surface energy budget indicates that the skin temperature anomalies as well as the trend, although very similar to that of the SAT, are produced primarily by downward longwave radiation.